Upload 6 files

module.py

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+import pandas as pd 
+def data_info(dataset):
+    try:
+        col_name = [] #save col name
+        Count    = []
+        Missing  = [] 
+        N_unique = [] 
+        Unique   = []
+        Dtype    = [] 
+        for col in dataset.columns:
+            col_name.append(col)
+            Count.append(dataset[col].count())
+            Missing.append(dataset[col].isna().sum())
+            N_unique.append(dataset[col].nunique())
+            Unique.append(dataset[col].unique())
+            Dtype.append(dataset[col].dtype)
+        return pd.DataFrame({"Col_Name" : col_name , "Dtype" : Dtype ,
+                             "N_unique" : N_unique , "Count" : Count , 
+                             "Missing"  : Missing  , "Unique": Unique  })
+    except Exception  as e :
+        print("Error: ",e)

placement.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df27a9aa2cb587e1ab67118bbc4fefd6bdce9525bb377c13070135ce502f8534
+size 937

placement_APP.py

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+import streamlit as st
+import pickle
+import numpy as np
+import matplotlib.pyplot as plt
+import requests
+import io
+
+@st.cache_resource
+def load_model():
+    url = "https://github.com/Ali-Abdelhamid-Ali/placement/blob/main/placement.pkl?raw=true"
+    response = requests.get(url)
+
+    if response.status_code == 200:
+        model = pickle.load(io.BytesIO(response.content)) 
+        return model
+    else:
+        st.error("Failed to download the model.")
+        return None
+
+best_model = load_model()
+
+def classify_placement(CGPA, Internships, Projects, Workshops_Certifications, AptitudeTestScore,
+                       SoftSkillsRating, ExtracurricularActivities, PlacementTraining, SSC_Marks, HSC_Marks):
+    input_data = (CGPA, Internships, Projects, Workshops_Certifications, AptitudeTestScore,
+                  SoftSkillsRating, ExtracurricularActivities, PlacementTraining, SSC_Marks, HSC_Marks)
+    
+    input_data_as_numpy_array = np.asarray(input_data)
+    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)
+    
+    prediction = best_model.predict(input_data_reshaped)
+    
+    return prediction
+
+def visualize_data(CGPA, prediction):
+    fig, ax = plt.subplots(figsize=(6, 4))
+    ax.bar(['Not Placed', 'Placed'], [1 - prediction, prediction], color=['red', 'green'])
+    ax.set_title(f'Placement Prediction for CGPA: {CGPA}')
+    ax.set_ylabel('Probability')
+    ax.set_ylim(0, 1)
+    st.pyplot(fig)
+
+def main():
+    st.title("Placement Prediction App")
+    st.write("Predicts student placement success based on academic performance and skills.")
+
+    CGPA = st.slider("CGPA", 0.0, 10.0, 7.0, 0.01)
+    Internships = st.slider("Number of Internships", 0, 20, 3)
+    Projects = st.slider("Number of Projects", 0, 100, 10)
+    Workshops_Certifications = st.slider("Number of Workshops and Certifications", 0, 10, 4)
+    AptitudeTestScore = st.slider("Aptitude Test Score", 0, 100, 70)
+    SoftSkillsRating = st.slider("Soft Skills Rating", 0, 5, 2)
+    ExtracurricularActivities = st.slider("Extracurricular Activities", 0, 5, 1)
+    PlacementTraining = st.slider("Placement Training", 1, 8, 0)
+    SSC_Marks = st.slider("SSC Marks", 0, 100, 75)
+    HSC_Marks = st.slider("HSC Marks", 0, 100, 80)
+    
+    if st.button("Predict"):
+        prediction = classify_placement(CGPA, Internships, Projects, Workshops_Certifications, AptitudeTestScore,
+                                       SoftSkillsRating, ExtracurricularActivities, PlacementTraining, SSC_Marks, HSC_Marks)
+        
+        if prediction[0] == 1:
+            st.write("The student will be placed.")
+        else:
+            st.markdown("The student will <span style='color:red;'>NOT</span> be placed.", unsafe_allow_html=True)
+        
+        visualize_data(CGPA, prediction[0])
+
+main()

requirements.txt

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+matplotlib
+numpy
+streamlit